A Scalable Bluetooth Low Energy Design Model for Sensor Detection for an Indoor Real Time Location System

Indoor Real Time Location Systems (RTLS) research identifies Bluetooth Low Energy as one of the technologies that promise an acceptable response to the requirements of the Healthcare environment. A scalable dynamic model for sensor detection, which uses the latest developments of Bluetooth Low Energy, is designed to extend its range coverage. This design extends on our previous papers which tested the range and signal strength through multiple types of obstructions. The model is based on the scenarios and use cases identified for future use in RTLS within the Health care sector. The Unified Modelling Language (UML) is used to present the models and inspections and walkthroughs are used to validate and verify them. This model will be implemented using Bluetooth Low Energy devices for patients and assets with in the Health care sector.

[1]  Erik Simmons.  The usage model: a structure for richly describing product usage during design and development , 2005, 13th IEEE International Conference on Requirements Engineering (RE'05).

[2]  Mostafa Bellafkih,et al.  Bluetooth Low Energy (BLE) based geomarketing system , 2016, 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA).

[3]  Prasant Misra,et al.  Building the Internet of Things with bluetooth smart , 2017, Ad Hoc Networks.

[4]  Yang Lei,et al.  Situation and development tendency of indoor positioning , 2013, China Communications.

[5]  Shulin Liu,et al.  Use Case and Non-functional Scenario Template-Based Approach to Identify Aspects , 2010, 2010 Second International Conference on Computer Engineering and Applications.

[6]  Bin Yu,et al.  Bluetooth Low Energy (BLE) based mobile electrocardiogram monitoring system , 2012, 2012 IEEE International Conference on Information and Automation.

[7]  Georg Kösters,et al.  Validation and Verification of Use Cases and Class Models , 2000 .

[8]  Cem Ersoy,et al.  Wireless sensor networks for healthcare: A survey , 2010, Comput. Networks.

[9]  Daniel Ostler,et al.  Testing a proximity-based location tracking system with Bluetooth Low Energy tags for future use in the OR , 2015, 2015 17th International Conference on E-health Networking, Application & Services (HealthCom).

[10]  Wen Yao,et al.  The Adoption and Implementation of RFID Technologies in Healthcare: A Literature Review , 2012, Journal of Medical Systems.

[11]  Qiang Ye,et al.  RSSI-Based Bluetooth Indoor Localization , 2015, 2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN).

[12]  Saleem Ahmad,et al.  Bluetooth an Optimal Solution for Personal Asset Tracking: A Comparison of Bluetooth, RFID and Miscellaneous Anti-lost Traking Technologies , 2015 .

[13]  Bashar Nuseibeh,et al.  Analysing anaphoric ambiguity in natural language requirements , 2011, Requirements Engineering.

[14]  Torin Monahan,et al.  Evaluation of real-time location systems in their hospital contexts , 2012, Int. J. Medical Informatics.

[15]  Mohamed El-Attar.  A systematic approach to assemble sequence diagrams from use case scenarios , 2011, 2011 3rd International Conference on Computer Research and Development.

[16]  Kevin Curran,et al.  A survey of active and passive indoor localisation systems , 2012, Comput. Commun..

[17]  Jay Pancham,et al.  Investigation of Obstructions and Range Limit on Bluetooth Low Energy RSSI for the Healthcare Environment , 2018, ICCSA.

[18]  Johan Hjelm,et al.  Local Positioning Systems: LBS Applications and Services , 2006 .

[19]  Jay Pancham,et al.  Evaluation of Real Time Location System technologies in the health care sector , 2017, 2017 17th International Conference on Computational Science and Its Applications (ICCSA).

[20]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..

[21]  Jay Pancham,et al.  Assessment of Feasible Methods Used by the Health Care Industry for Real Time Location , 2017, FedCSIS.

[22]  G. T. S. Ho,et al.  A Bluetooth-based Indoor Positioning System: a Simple and Rapid Approach , 2015 .

[23]  Jin-Shyan Lee,et al.  A preliminary study of low power wireless technologies: ZigBee and Bluetooth Low Energy , 2015, 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA).

[24]  Thierry Val,et al.  BLE localization using RSSI measurements and iRingLA , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[25]  Stephen R. Schach,et al.  Object-oriented and classical software engineering , 1995 .